Active Particle

Active particles, self-propelled entities ranging from microscopic colloids to bacteria, are being intensely studied to understand their collective behavior and harness their capabilities. Current research focuses on developing control algorithms, often employing deep reinforcement learning and novel neural network architectures like spatially-local transformers, to optimize their navigation and collective motion in complex environments. This work is driven by the potential for applications in areas such as micro-robotics, targeted drug delivery, and the development of novel computing paradigms based on self-organizing active matter systems. The development of efficient computational tools and the exploration of multi-objective optimization strategies are key themes in advancing this field.